Estimation of EV battery SOC based on KF dynamic neural network with GA

Power battery is a core energy system of electric vehicle (EV). The accurate state of charge (SOC) prediction is the most basic and important for the battery management system. There are many shortcomings in the traditional method of predicting SOC, such as the accumulation of errors with time and the difficulty of on-line prediction, etc. So a Dynamic Neural Network by Kalman filter (KDNN) is proposed to predict the SOC of electric vehicles. The weights of dynamic neural network is updated by KF. At the same time, in order to reduce the randomness of the prediction algorithm, the Genetic algorithm (GA) is used to optimize the initial weights of KDNN. Then the battery data from the experiment during the car driving are used to test the performance of KDNN with GA. And the comparisons are done between the new KDNN and BP prediction methods. The results demonstrate that the new KDNN with GA prediction modeling method has obvious superiority compared with BP neural network.